{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ccaa519-641c-4c68-a50a-668e17b9fe2d",
   "metadata": {},
   "source": [
    "# Legend Key Parameters in `theme()`\n",
    "New parameters in `theme()` to customize the legend key:\n",
    "\n",
    "- `legendKey` - background underneath legend keys, set with `elementRect()`\n",
    "- `legendKeySize` - size of legend keys\n",
    "- `legendKeyWidth` - key background width\n",
    "- `legendKeyHeight` - key background height\n",
    "- `legendKeySpacing` - spacing between legend keys\n",
    "- `legendKeySpacingX` - spacing in the horizontal direction\n",
    "- `legendKeySpacingY` - spacing in the vertical direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "503686e9-e057-4afe-a003-475fe00d4c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"lOCGwu\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "       if(!window.letsPlotCallQueue) {\n",
       "           window.letsPlotCallQueue = [];\n",
       "       }; \n",
       "       window.letsPlotCall = function(f) {\n",
       "           window.letsPlotCallQueue.push(f);\n",
       "       };\n",
       "       (function() {\n",
       "           var script = document.createElement(\"script\");\n",
       "           script.type = \"text/javascript\";\n",
       "           script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.5.1/js-package/distr/lets-plot.min.js\";\n",
       "           script.onload = function() {\n",
       "               window.letsPlotCall = function(f) {f();};\n",
       "               window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "               window.letsPlotCallQueue = [];\n",
       "               \n",
       "               \n",
       "           };\n",
       "           script.onerror = function(event) {\n",
       "               window.letsPlotCall = function(f) {};\n",
       "               window.letsPlotCallQueue = [];\n",
       "               var div = document.createElement(\"div\");\n",
       "               div.style.color = 'darkred';\n",
       "               div.textContent = 'Error loading Lets-Plot JS';\n",
       "               document.getElementById(\"lOCGwu\").appendChild(div);\n",
       "           };\n",
       "           var e = document.getElementById(\"lOCGwu\");\n",
       "           e.appendChild(script);\n",
       "       })();\n",
       "   </script>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "            <div id=\"kotlin_out_0\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                            if(!window.kotlinQueues) {\n",
       "                window.kotlinQueues = {};\n",
       "            }\n",
       "            if(!window.kotlinQueues[\"DataFrame\"]) {\n",
       "                var resQueue = [];\n",
       "                window.kotlinQueues[\"DataFrame\"] = resQueue;\n",
       "                window[\"call_DataFrame\"] = function(f) {\n",
       "                    resQueue.push(f);\n",
       "                }\n",
       "            }\n",
       "            (function (){\n",
       "                var modifiers = [(function(script) {\n",
       "    script.src = \"https://cdn.jsdelivr.net/gh/Kotlin/dataframe@3db46ccccaa1291c0627307d64133317f545e6ae/core/src/main/resources/init.js\"\n",
       "    script.type = \"text/javascript\";\n",
       "})];\n",
       "                var e = document.getElementById(\"kotlin_out_0\");\n",
       "                modifiers.forEach(function (gen) {\n",
       "                    var script = document.createElement(\"script\");\n",
       "                    gen(script)\n",
       "                    script.addEventListener(\"load\", function() {\n",
       "                        window[\"call_DataFrame\"] = function(f) {f();};\n",
       "                        window.kotlinQueues[\"DataFrame\"].forEach(function(f) {f();});\n",
       "                        window.kotlinQueues[\"DataFrame\"] = [];\n",
       "                    }, false);\n",
       "                    script.addEventListener(\"error\", function() {\n",
       "                        window[\"call_DataFrame\"] = function(f) {};\n",
       "                        window.kotlinQueues[\"DataFrame\"] = [];\n",
       "                        var div = document.createElement(\"div\");\n",
       "                        div.style.color = 'darkred';\n",
       "                        div.textContent = 'Error loading resource DataFrame';\n",
       "                        document.getElementById(\"kotlin_out_0\").appendChild(div);\n",
       "                    }, false);\n",
       "                    \n",
       "                    e.appendChild(script);\n",
       "                });\n",
       "            })();\n",
       "            </script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "                <style>\n",
       "                :root {\n",
       "    --background: #fff;\n",
       "    --background-odd: #f5f5f5;\n",
       "    --background-hover: #d9edfd;\n",
       "    --header-text-color: #474747;\n",
       "    --text-color: #848484;\n",
       "    --text-color-dark: #000;\n",
       "    --text-color-medium: #737373;\n",
       "    --text-color-pale: #b3b3b3;\n",
       "    --inner-border-color: #aaa;\n",
       "    --bold-border-color: #000;\n",
       "    --link-color: #296eaa;\n",
       "    --link-color-pale: #296eaa;\n",
       "    --link-hover: #1a466c;\n",
       "}\n",
       "\n",
       ":root[theme=\"dark\"], :root [data-jp-theme-light=\"false\"], .dataframe_dark{\n",
       "    --background: #303030;\n",
       "    --background-odd: #3c3c3c;\n",
       "    --background-hover: #464646;\n",
       "    --header-text-color: #dddddd;\n",
       "    --text-color: #b3b3b3;\n",
       "    --text-color-dark: #dddddd;\n",
       "    --text-color-medium: #b2b2b2;\n",
       "    --text-color-pale: #737373;\n",
       "    --inner-border-color: #707070;\n",
       "    --bold-border-color: #777777;\n",
       "    --link-color: #008dc0;\n",
       "    --link-color-pale: #97e1fb;\n",
       "    --link-hover: #00688e;\n",
       "}\n",
       "\n",
       "p.dataframe_description {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe {\n",
       "    font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n",
       "    font-size: 12px;\n",
       "    background-color: var(--background);\n",
       "    color: var(--text-color-dark);\n",
       "    border: none;\n",
       "    border-collapse: collapse;\n",
       "}\n",
       "\n",
       "table.dataframe th, td {\n",
       "    padding: 6px;\n",
       "    border: 1px solid transparent;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       "table.dataframe th {\n",
       "    background-color: var(--background);\n",
       "    color: var(--header-text-color);\n",
       "}\n",
       "\n",
       "table.dataframe td {\n",
       "    vertical-align: top;\n",
       "}\n",
       "\n",
       "table.dataframe th.bottomBorder {\n",
       "    border-bottom-color: var(--bold-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:nth-child(odd) {\n",
       "    background: var(--background-odd);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:nth-child(even) {\n",
       "    background: var(--background);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:hover {\n",
       "    background: var(--background-hover);\n",
       "}\n",
       "\n",
       "table.dataframe a {\n",
       "    cursor: pointer;\n",
       "    color: var(--link-color);\n",
       "    text-decoration: none;\n",
       "}\n",
       "\n",
       "table.dataframe tr:hover > td a {\n",
       "    color: var(--link-color-pale);\n",
       "}\n",
       "\n",
       "table.dataframe a:hover {\n",
       "    color: var(--link-hover);\n",
       "    text-decoration: underline;\n",
       "}\n",
       "\n",
       "table.dataframe img {\n",
       "    max-width: fit-content;\n",
       "}\n",
       "\n",
       "table.dataframe th.complex {\n",
       "    background-color: var(--background);\n",
       "    border: 1px solid var(--background);\n",
       "}\n",
       "\n",
       "table.dataframe .leftBorder {\n",
       "    border-left-color: var(--inner-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe .rightBorder {\n",
       "    border-right-color: var(--inner-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe .rightAlign {\n",
       "    text-align: right;\n",
       "}\n",
       "\n",
       "table.dataframe .expanderSvg {\n",
       "    width: 8px;\n",
       "    height: 8px;\n",
       "    margin-right: 3px;\n",
       "}\n",
       "\n",
       "table.dataframe .expander {\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "}\n",
       "\n",
       "/* formatting */\n",
       "\n",
       "table.dataframe .null {\n",
       "    color: var(--text-color-pale);\n",
       "}\n",
       "\n",
       "table.dataframe .structural {\n",
       "    color: var(--text-color-medium);\n",
       "    font-weight: bold;\n",
       "}\n",
       "\n",
       "table.dataframe .dataFrameCaption {\n",
       "    font-weight: bold;\n",
       "}\n",
       "\n",
       "table.dataframe .numbers {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe td:hover .formatted .structural, .null {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe tr:hover .formatted .structural, .null {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "\n",
       "                </style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%useLatestDescriptors\n",
    "%use lets-plot\n",
    "\n",
    "%use dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c4138f8c-c0d6-49d7-ba48-b30a55d5df68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lets-Plot Kotlin API v.4.9.0. Frontend: Notebook with dynamically loaded JS. Lets-Plot JS v.4.5.1."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LetsPlot.getInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3bfa73c5-910a-42a2-bb17-b154ea3080fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "class java.awt.image.BufferedImage: 2000x1600 px"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import javax.imageio.ImageIO\n",
    "import java.net.URL\n",
    "\n",
    "val url = URL(\"https://github.com/JetBrains/lets-plot/raw/master/docs/f-24g/images/theme_legend_scheme.png\")\n",
    "ImageIO.read(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b6b8988b-e5ba-4fda-a322-ad1d215e45c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/kotlindataframe+json": "{\"nrow\":3,\"ncol\":12,\"columns\":[\"untitled\",\"manufacturer\",\"model\",\"displ\",\"year\",\"cyl\",\"trans\",\"drv\",\"cty\",\"hwy\",\"fl\",\"class\"],\"kotlin_dataframe\":[{\"untitled\":1,\"manufacturer\":\"audi\",\"model\":\"a4\",\"displ\":1.8,\"year\":1999,\"cyl\":4,\"trans\":\"auto(l5)\",\"drv\":\"f\",\"cty\":18,\"hwy\":29,\"fl\":\"p\",\"class\":\"compact\"},{\"untitled\":2,\"manufacturer\":\"audi\",\"model\":\"a4\",\"displ\":1.8,\"year\":1999,\"cyl\":4,\"trans\":\"manual(m5)\",\"drv\":\"f\",\"cty\":21,\"hwy\":29,\"fl\":\"p\",\"class\":\"compact\"},{\"untitled\":3,\"manufacturer\":\"audi\",\"model\":\"a4\",\"displ\":2.0,\"year\":2008,\"cyl\":4,\"trans\":\"manual(m6)\",\"drv\":\"f\",\"cty\":20,\"hwy\":31,\"fl\":\"p\",\"class\":\"compact\"}]}",
      "text/html": [
       "        <html>\n",
       "        <head>\n",
       "            <style type=\"text/css\">\n",
       "                :root {\n",
       "    --background: #fff;\n",
       "    --background-odd: #f5f5f5;\n",
       "    --background-hover: #d9edfd;\n",
       "    --header-text-color: #474747;\n",
       "    --text-color: #848484;\n",
       "    --text-color-dark: #000;\n",
       "    --text-color-medium: #737373;\n",
       "    --text-color-pale: #b3b3b3;\n",
       "    --inner-border-color: #aaa;\n",
       "    --bold-border-color: #000;\n",
       "    --link-color: #296eaa;\n",
       "    --link-color-pale: #296eaa;\n",
       "    --link-hover: #1a466c;\n",
       "}\n",
       "\n",
       ":root[theme=\"dark\"], :root [data-jp-theme-light=\"false\"], .dataframe_dark{\n",
       "    --background: #303030;\n",
       "    --background-odd: #3c3c3c;\n",
       "    --background-hover: #464646;\n",
       "    --header-text-color: #dddddd;\n",
       "    --text-color: #b3b3b3;\n",
       "    --text-color-dark: #dddddd;\n",
       "    --text-color-medium: #b2b2b2;\n",
       "    --text-color-pale: #737373;\n",
       "    --inner-border-color: #707070;\n",
       "    --bold-border-color: #777777;\n",
       "    --link-color: #008dc0;\n",
       "    --link-color-pale: #97e1fb;\n",
       "    --link-hover: #00688e;\n",
       "}\n",
       "\n",
       "p.dataframe_description {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe {\n",
       "    font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n",
       "    font-size: 12px;\n",
       "    background-color: var(--background);\n",
       "    color: var(--text-color-dark);\n",
       "    border: none;\n",
       "    border-collapse: collapse;\n",
       "}\n",
       "\n",
       "table.dataframe th, td {\n",
       "    padding: 6px;\n",
       "    border: 1px solid transparent;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       "table.dataframe th {\n",
       "    background-color: var(--background);\n",
       "    color: var(--header-text-color);\n",
       "}\n",
       "\n",
       "table.dataframe td {\n",
       "    vertical-align: top;\n",
       "}\n",
       "\n",
       "table.dataframe th.bottomBorder {\n",
       "    border-bottom-color: var(--bold-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:nth-child(odd) {\n",
       "    background: var(--background-odd);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:nth-child(even) {\n",
       "    background: var(--background);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:hover {\n",
       "    background: var(--background-hover);\n",
       "}\n",
       "\n",
       "table.dataframe a {\n",
       "    cursor: pointer;\n",
       "    color: var(--link-color);\n",
       "    text-decoration: none;\n",
       "}\n",
       "\n",
       "table.dataframe tr:hover > td a {\n",
       "    color: var(--link-color-pale);\n",
       "}\n",
       "\n",
       "table.dataframe a:hover {\n",
       "    color: var(--link-hover);\n",
       "    text-decoration: underline;\n",
       "}\n",
       "\n",
       "table.dataframe img {\n",
       "    max-width: fit-content;\n",
       "}\n",
       "\n",
       "table.dataframe th.complex {\n",
       "    background-color: var(--background);\n",
       "    border: 1px solid var(--background);\n",
       "}\n",
       "\n",
       "table.dataframe .leftBorder {\n",
       "    border-left-color: var(--inner-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe .rightBorder {\n",
       "    border-right-color: var(--inner-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe .rightAlign {\n",
       "    text-align: right;\n",
       "}\n",
       "\n",
       "table.dataframe .expanderSvg {\n",
       "    width: 8px;\n",
       "    height: 8px;\n",
       "    margin-right: 3px;\n",
       "}\n",
       "\n",
       "table.dataframe .expander {\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "}\n",
       "\n",
       "/* formatting */\n",
       "\n",
       "table.dataframe .null {\n",
       "    color: var(--text-color-pale);\n",
       "}\n",
       "\n",
       "table.dataframe .structural {\n",
       "    color: var(--text-color-medium);\n",
       "    font-weight: bold;\n",
       "}\n",
       "\n",
       "table.dataframe .dataFrameCaption {\n",
       "    font-weight: bold;\n",
       "}\n",
       "\n",
       "table.dataframe .numbers {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe td:hover .formatted .structural, .null {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe tr:hover .formatted .structural, .null {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "\n",
       ":root {\n",
       "    --background: #fff;\n",
       "    --background-odd: #f5f5f5;\n",
       "    --background-hover: #d9edfd;\n",
       "    --header-text-color: #474747;\n",
       "    --text-color: #848484;\n",
       "    --text-color-dark: #000;\n",
       "    --text-color-medium: #737373;\n",
       "    --text-color-pale: #b3b3b3;\n",
       "    --inner-border-color: #aaa;\n",
       "    --bold-border-color: #000;\n",
       "    --link-color: #296eaa;\n",
       "    --link-color-pale: #296eaa;\n",
       "    --link-hover: #1a466c;\n",
       "}\n",
       "\n",
       ":root[theme=\"dark\"], :root [data-jp-theme-light=\"false\"], .dataframe_dark{\n",
       "    --background: #303030;\n",
       "    --background-odd: #3c3c3c;\n",
       "    --background-hover: #464646;\n",
       "    --header-text-color: #dddddd;\n",
       "    --text-color: #b3b3b3;\n",
       "    --text-color-dark: #dddddd;\n",
       "    --text-color-medium: #b2b2b2;\n",
       "    --text-color-pale: #737373;\n",
       "    --inner-border-color: #707070;\n",
       "    --bold-border-color: #777777;\n",
       "    --link-color: #008dc0;\n",
       "    --link-color-pale: #97e1fb;\n",
       "    --link-hover: #00688e;\n",
       "}\n",
       "\n",
       "p.dataframe_description {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe {\n",
       "    font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n",
       "    font-size: 12px;\n",
       "    background-color: var(--background);\n",
       "    color: var(--text-color-dark);\n",
       "    border: none;\n",
       "    border-collapse: collapse;\n",
       "}\n",
       "\n",
       "table.dataframe th, td {\n",
       "    padding: 6px;\n",
       "    border: 1px solid transparent;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       "table.dataframe th {\n",
       "    background-color: var(--background);\n",
       "    color: var(--header-text-color);\n",
       "}\n",
       "\n",
       "table.dataframe td {\n",
       "    vertical-align: top;\n",
       "}\n",
       "\n",
       "table.dataframe th.bottomBorder {\n",
       "    border-bottom-color: var(--bold-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:nth-child(odd) {\n",
       "    background: var(--background-odd);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:nth-child(even) {\n",
       "    background: var(--background);\n",
       "}\n",
       "\n",
       "table.dataframe tbody > tr:hover {\n",
       "    background: var(--background-hover);\n",
       "}\n",
       "\n",
       "table.dataframe a {\n",
       "    cursor: pointer;\n",
       "    color: var(--link-color);\n",
       "    text-decoration: none;\n",
       "}\n",
       "\n",
       "table.dataframe tr:hover > td a {\n",
       "    color: var(--link-color-pale);\n",
       "}\n",
       "\n",
       "table.dataframe a:hover {\n",
       "    color: var(--link-hover);\n",
       "    text-decoration: underline;\n",
       "}\n",
       "\n",
       "table.dataframe img {\n",
       "    max-width: fit-content;\n",
       "}\n",
       "\n",
       "table.dataframe th.complex {\n",
       "    background-color: var(--background);\n",
       "    border: 1px solid var(--background);\n",
       "}\n",
       "\n",
       "table.dataframe .leftBorder {\n",
       "    border-left-color: var(--inner-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe .rightBorder {\n",
       "    border-right-color: var(--inner-border-color);\n",
       "}\n",
       "\n",
       "table.dataframe .rightAlign {\n",
       "    text-align: right;\n",
       "}\n",
       "\n",
       "table.dataframe .expanderSvg {\n",
       "    width: 8px;\n",
       "    height: 8px;\n",
       "    margin-right: 3px;\n",
       "}\n",
       "\n",
       "table.dataframe .expander {\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "}\n",
       "\n",
       "/* formatting */\n",
       "\n",
       "table.dataframe .null {\n",
       "    color: var(--text-color-pale);\n",
       "}\n",
       "\n",
       "table.dataframe .structural {\n",
       "    color: var(--text-color-medium);\n",
       "    font-weight: bold;\n",
       "}\n",
       "\n",
       "table.dataframe .dataFrameCaption {\n",
       "    font-weight: bold;\n",
       "}\n",
       "\n",
       "table.dataframe .numbers {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe td:hover .formatted .structural, .null {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "table.dataframe tr:hover .formatted .structural, .null {\n",
       "    color: var(--text-color-dark);\n",
       "}\n",
       "\n",
       "\n",
       "            </style>\n",
       "        </head>\n",
       "        <body>\n",
       "            <table class=\"dataframe\" id=\"df_-1761607680\"></table>\n",
       "\n",
       "<p class=\"dataframe_description\">DataFrame: rowsCount = 3, columnsCount = 12</p>\n",
       "<table class=\"dataframe\" id=\"static_df_-1761607679\"><thead><tr><th class=\"bottomBorder\" style=\"text-align:left\">untitled</th><th class=\"bottomBorder\" style=\"text-align:left\">manufacturer</th><th class=\"bottomBorder\" style=\"text-align:left\">model</th><th class=\"bottomBorder\" style=\"text-align:left\">displ</th><th class=\"bottomBorder\" style=\"text-align:left\">year</th><th class=\"bottomBorder\" style=\"text-align:left\">cyl</th><th class=\"bottomBorder\" style=\"text-align:left\">trans</th><th class=\"bottomBorder\" style=\"text-align:left\">drv</th><th class=\"bottomBorder\" style=\"text-align:left\">cty</th><th class=\"bottomBorder\" style=\"text-align:left\">hwy</th><th class=\"bottomBorder\" style=\"text-align:left\">fl</th><th class=\"bottomBorder\" style=\"text-align:left\">class</th></tr></thead><tbody><tr><td  style=\"vertical-align:top\">1</td><td  style=\"vertical-align:top\">audi</td><td  style=\"vertical-align:top\">a4</td><td  style=\"vertical-align:top\">1.800000</td><td  style=\"vertical-align:top\">1999</td><td  style=\"vertical-align:top\">4</td><td  style=\"vertical-align:top\">auto(l5)</td><td  style=\"vertical-align:top\">f</td><td  style=\"vertical-align:top\">18</td><td  style=\"vertical-align:top\">29</td><td  style=\"vertical-align:top\">p</td><td  style=\"vertical-align:top\">compact</td></tr><tr><td  style=\"vertical-align:top\">2</td><td  style=\"vertical-align:top\">audi</td><td  style=\"vertical-align:top\">a4</td><td  style=\"vertical-align:top\">1.800000</td><td  style=\"vertical-align:top\">1999</td><td  style=\"vertical-align:top\">4</td><td  style=\"vertical-align:top\">manual(m5)</td><td  style=\"vertical-align:top\">f</td><td  style=\"vertical-align:top\">21</td><td  style=\"vertical-align:top\">29</td><td  style=\"vertical-align:top\">p</td><td  style=\"vertical-align:top\">compact</td></tr><tr><td  style=\"vertical-align:top\">3</td><td  style=\"vertical-align:top\">audi</td><td  style=\"vertical-align:top\">a4</td><td  style=\"vertical-align:top\">2.000000</td><td  style=\"vertical-align:top\">2008</td><td  style=\"vertical-align:top\">4</td><td  style=\"vertical-align:top\">manual(m6)</td><td  style=\"vertical-align:top\">f</td><td  style=\"vertical-align:top\">20</td><td  style=\"vertical-align:top\">31</td><td  style=\"vertical-align:top\">p</td><td  style=\"vertical-align:top\">compact</td></tr></tbody></table>\n",
       "        </body>\n",
       "        <script>\n",
       "            /*<!--*/\n",
       "call_DataFrame(function() { DataFrame.addTable({ cols: [{ name: \"<span title=\\\"untitled: Int\\\">untitled</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">1</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">2</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">3</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"manufacturer: String\\\">manufacturer</span>\", children: [], rightAlign: false, values: [\"audi\",\"audi\",\"audi\"] }, \n",
       "{ name: \"<span title=\\\"model: String\\\">model</span>\", children: [], rightAlign: false, values: [\"a4\",\"a4\",\"a4\"] }, \n",
       "{ name: \"<span title=\\\"displ: Double\\\">displ</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">1.8</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">1.8</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">2.0</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"year: Int\\\">year</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">1999</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">1999</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">2008</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"cyl: Int\\\">cyl</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">4</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">4</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">4</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"trans: String\\\">trans</span>\", children: [], rightAlign: false, values: [\"auto(l5)\",\"manual(m5)\",\"manual(m6)\"] }, \n",
       "{ name: \"<span title=\\\"drv: String\\\">drv</span>\", children: [], rightAlign: false, values: [\"f\",\"f\",\"f\"] }, \n",
       "{ name: \"<span title=\\\"cty: Int\\\">cty</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">18</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">21</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">20</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"hwy: Int\\\">hwy</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">29</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">29</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">31</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"fl: String\\\">fl</span>\", children: [], rightAlign: false, values: [\"p\",\"p\",\"p\"] }, \n",
       "{ name: \"<span title=\\\"class: String\\\">class</span>\", children: [], rightAlign: false, values: [\"compact\",\"compact\",\"compact\"] }, \n",
       "], id: -1761607680, rootId: -1761607680, totalRows: 3 } ) });\n",
       "/*-->*/\n",
       "\n",
       "call_DataFrame(function() { DataFrame.renderTable(-1761607680) });\n",
       "\n",
       "document.getElementById(\"static_df_-1761607679\").style.display = \"none\";\n",
       "        </script>\n",
       "        </html>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val mpg = DataFrame.readCSV(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
    "mpg.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b416d09-e400-4142-a929-dc07b8f6aeca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "               // Render plot\n",
       "               if (observer) {\n",
       "                   observer.disconnect();\n",
       "                   observer = null;\n",
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       "\n",
       "               var plotSpec={\n",
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       "\"legend_background\":{\n",
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       "\"data_meta\":{\n",
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       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"spec_id\":\"1\"\n",
       "};\n",
       "               window.letsPlotCall(function() {\n",
       "       \n",
       "               var toolbar = null;\n",
       "               var plotContainer = containerDiv;               \n",
       "               \n",
       "                   var options = {\n",
       "                       sizing: {\n",
       "                           width_mode: \"min\",\n",
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       "                   var fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer, options);\n",
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       "                     toolbar.bind(fig);\n",
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       "   })();\n",
       "   \n",
       "   </script>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val p = letsPlot(mpg.toMap()) { x = \"hwy\" } +\n",
    "    geomDotplot(color=\"pen\") { fill = \"class\" } +\n",
    "    theme(legendBackground = elementRect(size = 0.5))\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94f5e35-93c0-4265-93ef-49829452e64d",
   "metadata": {},
   "source": [
    "Add background underneath legend keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d26cb7e-7793-4bdb-8d45-914f1fb7b202",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   var containerDiv = document.getElementById(\"v4r6wb\");\n",
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       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
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       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"spec_id\":\"2\"\n",
       "};\n",
       "               window.letsPlotCall(function() {\n",
       "       \n",
       "               var toolbar = null;\n",
       "               var plotContainer = containerDiv;               \n",
       "               \n",
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       "                   var fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer, options);\n",
       "                   if (toolbar) {\n",
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       "               });\n",
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "val p1 = p + theme(legendKey = elementRect(fill = \"#efedf5\", color = \"#756bb1\"))\n",
    "p1"
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   "cell_type": "markdown",
   "id": "405916ac-1406-4e76-aab0-1d04d34d5b65",
   "metadata": {},
   "source": [
    "Change size of legend keys:"
   ]
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   "execution_count": 7,
   "id": "10036c57-9cb6-4a35-9e8f-525c3ed497c5",
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "p1 + theme(legendKeySize = 30)"
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   "execution_count": 8,
   "id": "3f115b4e-47a3-4fdb-b300-0519870c2ef7",
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   "metadata": {},
   "source": [
    "Add spacing between legend keys:"
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   "execution_count": 9,
   "id": "8c54e347-88fc-48a0-a75b-230b9a952fc1",
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       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"spec_id\":\"5\"\n",
       "};\n",
       "               window.letsPlotCall(function() {\n",
       "       \n",
       "               var toolbar = null;\n",
       "               var plotContainer = containerDiv;               \n",
       "               \n",
       "                   var options = {\n",
       "                       sizing: {\n",
       "                           width_mode: \"min\",\n",
       "                           height_mode: \"scaled\",\n",
       "                           width: width\n",
       "                       }\n",
       "                   };\n",
       "                   var fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer, options);\n",
       "                   if (toolbar) {\n",
       "                     toolbar.bind(fig);\n",
       "                   }\n",
       "               });\n",
       "               \n",
       "               break;\n",
       "           }\n",
       "       }\n",
       "   });\n",
       "   \n",
       "   observer.observe(containerDiv);\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2 + theme(legendKeySpacingY = 10)"
   ]
  }
 ],
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